ablaom / RAPIDS.jl

A Unofficial Julia wrapper for the RAPIDS.ai ecosystem

RAPIDS.jl

Unofficial Julia wrapper for the RAPIDS.ai ecosystem. Support is limited to Linux.

The goal of this library is to provide a simple method for accessing the GPU accelerated models withing RAPIDS from Julia, and integrating the models into MLJ. This library relies on PythonCall.jl and CondaPkg.jl for efficient installations of the Python dependencies.

This wrapper could be broken up into several libraries (cuDF, cuML, cuGraph, cuSignal, cuSpatial), but there would be significant overlap between these libraries. Large dependencies such as cudatoolkit would be repeated.

Installation

From source:

julia> ]add https://github.com/tylerjthomas9/RAPIDS.jl

julia> using Pkg; Pkg.add(url="https://github.com/tylerjthomas9/RAPIDS.jl")

Python API

You can access the following python libraries with their standard syntax:

• cupy
• cudf
• cuml
• cugraph
• cusignal
• cuspatial
• dask
• dask_cuda
• dask_cudf
• numpy
• pickle

Here is an example of using LogisticRegression, make_classification via the Python API.

using RAPIDS
const make_classification = cuml.datasets.classification.make_classification

X_py, y_py = make_classification(n_samples=200, n_features=4,
                           n_informative=2, n_classes=2)
lr = cuml.LogisticRegression(max_iter=100)
lr.fit(X_py, y_py)
preds = lr.predict(X_py)

print(lr.coef_)

MLJ Interface

A MLJ interface is also available for supported models. The model hyperparameters are the same as described in the cuML docs. The only difference is that the models will always input/output numpy arrays, which will be converted back to Julia arrays (output_type="input").

using MLJ
using RAPIDS
const make_classification = cuml.datasets.classification.make_classification

X_py, y_py = make_classification(n_samples=200, n_features=4,
                           n_informative=2, n_classes=2)
X = RAPIDS.pyconvert(Matrix{Float32}, X_py.get())
y = RAPIDS.pyconvert(Vector{Float32}, y_py.get().flatten())

lr = LogisticRegression(max_iter=100)
mach = machine(lr, X, y)
fit!(mach)
preds = predict(mach, X)

print(mach.fitresult.coef_)

MLJ Support:

• Clustering
  • KMeans
  • DBSCAN
  • AgglomerativeClustering
  • HDBSCAN
• Classification
  • LogisticRegression
  • MBSGDClassifier
  • KNeighborsClassifier
• Regression
  • LinearRegression
  • Ridge
  • Lasso
  • ElasticNet
  • MBSGDRegressor
  • RandomForestRegressor
  • CD
  • SVR
  • LinearSVR
  • KNeighborsRegressor
• Dimensionality Reduction
  • PCA
  • IncrementalPCA
  • TruncatedSVD
  • UMAP
  • TSNE
  • GaussianRandomProjection
• Time Series
  • ExponentialSmoothing